Characterization of a 4 lncRNAs-based prognostic risk scoring system in adults with acute myeloid leukemia

Prospective Identification of Prognostic Hot-Spot Mutant Gene Signatures for Leukemia: A Computational Study Based on Integrative Analysis of TCGA and cBioPortal Data

The advantage of an increasing amount of bioinformatics data on leukemias intrigued us to explore the hot-spot mutation profiles and investigate the implications of those hot-spot mutations in patient survival. We retrieved somatic mutations and their distribution in protein domains through data analysis of The Cancer Genome Atlas and cBioPortal databases. After determining differentially expressed mutant genes related to leukemia, we further conducted principal component analysis and single-factor Cox regression analyses. Moreover, survival analysis was performed for the obtained candidate genes, followed by a multi-factor Cox proportional hazard model method for the impacts of the candidate genes on the survival and prognosis of patients with leukemia. At last, the signaling pathways involved in leukemia were investigated by gene set enrichment analysis. There were 223 somatic missense mutation hot-spots identified with pertinence to leukemia, which were distributed in 41 genes. Differential expression in leukemia was witnessed in 39 genes. We found a close correlation between seven genes and the prognosis of leukemia patients, among which, three genes could significantly influence the survival rate. In addition, among these three genes, CD74 and P2RY8 were highlighted due to close pertinence with survival conditions of leukemia patients. Finally, data suggested that B cell receptor, Hedgehog, and TGF-beta signaling pathways were enriched in low-hazard patients. In conclusion, these data underline the involvement of hot-spot mutations of CD74 and P2RY8 genes in survival status of leukemia patients, highlighting their as novel therapeutic targets or prognostic indicators for leukemia patients. Summary of Graphical Abstract: We identified 223 leukemia-associated somatic missense mutation hotspots concentrated in 41 different genes from 2297 leukemia patients in the TCGA database. Differential analysis of leukemic and normal samples from the TCGA and GTEx databases revealed that 39 of these 41 genes showed significant differential expression in leukemia. These 39 genes were subjected to PCA analysis, univariate Cox analysis, survival analysis, multivariate Cox regression analysis, GSEA pathway enrichment analysis, and then the association with leukemia survival prognosis and related pathways were investigated.

The signature based on seven genomic instability-related genes could predict the prognosis of acute myeloid leukemia patients

BACKGROUND

Acute myeloid leukemia (AML) is the most common acute blood malignancy in adults. The complicated and dynamic genomic instability (GI) is the most prominent feature of AML. Our study aimed to explore the prognostic value of GI-related genes in AML patients.

METHODS

The mRNA data and mutation data were downloaded from the TCGA and GEO databases. Differential expression analyses were completed in limma package. GO and KEGG functional enrichment was conducted using clusterProfiler function of R. Univariate Cox and LASSO Cox regression analyses were performed to screen key genes for Risk score model construction. Nomogram was built with rms package.

RESULTS

We identified 114 DEGs between high TMB patients and low TMB AML patients, which were significantly enriched in 429 GO terms and 13 KEGG pathways. Based on the univariate Cox and LASSO Cox regression analyses, seven optimal genes were finally applied for Risk score model construction, including SELE, LGALS1, ITGAX, TMEM200A, SLC25A21, S100A4 and CRIP1. The Risk score could reliably predict the prognosis of AML patients. Age and Risk score were both independent prognostic indicators for AML, and the Nomogram based on them could also reliably predict the OS of AML patients.

CONCLUSIONS

A prognostic signature based on seven GI-related genes and a predictive Nomogram for AML patients are finally successfully constructed.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia

Background

Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML).

Methods

RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated.

Results

Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents.

Conclusion

A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.

Sevoflurane Inhibits lncRNA HOTAIR-Modulated Stability of HK2 mRNA in a m6A-Dependent Manner to Dampen Aerobic Glycolysis and Proliferation in Lung Cancer

Previous studies have shown that sevoflurane has an inhibitory effect on tumor cells. So far, the effect of sevoflurane on hepatocellular carcinoma needs to be confirmed by more studies. HOX transcript antisense intergenic RNA (HOTAIR), a long noncoding RNA (lncRNA), has been shown to enhance cancer cell proliferation and medication resistance. The inherent importance and biological function of HOTAIR in the course of lung cancer (LC) is, however, poorly unclear. HOTAIR was shown to be highly elevated in LC cells in this investigation. Impairment of function trials with sevoflurane indicated that it has anticancer effects on LC cell growth, apoptosis, and aerobic glycolysis. In a mechanistic manner, HOTAIR was related to HK2 mRNA and promoted expression and constancy. Additional research revealed that HOTAIR coupled with hexokinase 2 (HK2) mRNA and favorably controlled its stabilization in a traditional-component way. By HK2, the LC enhancement role was mediated. In summary, our data show that HOTAIR promotes the synthesis and proliferation of LC glycogen by increasing the transcription of HK2, and HOTAIR is likely to be a potential treatment for LC patients.

C-myc/TSPEAR-AS2 Axis Facilitates Breast Cancer Growth and Metastasis in a GLUT1-Dependent Glycolysis Manner

A large number of facts have shown that epigenetic modification and metabolic reprogramming represented by noncoding RNA play an important role in the invasion and metastasis of breast cancer, but the mechanism is not clear. The purpose of our study is to find a new biomarker of breast cancer and to provide a new perspective for regulating glucose metabolism and aerobic glycolysis of BC. In this paper, by downregulating C-myc protein, our team found that the expression of long-chain noncoding RNATSPAR-AS2 was significantly downregulated. However, the expression of long-chain noncoding RNASPAR-AS2 in BC is relatively high, and the prognosis is poor. TSPEAR-AS2 can promote the malignant phenotype of BC cells, including proliferation, apoptosis, invasion and metastasis, and glycolysis. At the same time, TSPEAR-AS2 can also upregulate the expression of GLUT1, an important regulator of glycolysis, thus promoting the metabolic reprogramming of BC. Molecular mechanism experiments show that TSPEAR-AS2 may promote the expression of GLUT1 by participating in IGF2BP2 modified by the GLUT1 gene. Our results suggest that the C-myc/TSPEAR-AS2/GLUT1 axis promotes the invasion and metastasis of BC by inducing glucose metabolism reprogramming. However, more phenotypic and molecular mechanism results need to be further verified.

Development of a Prognostic Nomogram for Acute Myeloid Leukemia on IGHD Gene Family

Purpose

Acute myeloid leukaemia (AML) is a common haematological disease in adults. The overall survival (OS) remains unsatisfactory. It is critical to identify potential prognostic biomarkers and develop a nomogram that predicts overall survival in patients with AML.

Patients and Methods

We used gene expression dataset and clinical data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify differential expression analysis, survival analysis, and prognostic value of IGHD gene family (IGHDs) in AML patients. A risk score model was built through Lasso analysis and multivariate Cox regression. We also developed a nomogram and evaluated its accuracy with Harrell’s Harmony Index (C-index) and calibration curve. Last, the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database was used for external validation.

Results

IGHD1-20 mRNA expression level was an independent prognostic factor for patients with AML by multivariate analysis. After Lasso analysis and multivariate Cox regression, we constructed a 3-gene model (IGHD1-1, IGHD1-20, IGHD3-16) associated with OS in AML. Risk score and age were validated as independent risk factors for prognosis and were used to build a nomogram. The C index and calibration curve results show that its ability to predict 1-year, 3-year and 5-year overall survival is accurate.

Conclusion

The mRNA level of IGHDs was increased in AML patients. IGHD1-20 was an independent risk factor for OS in AML patients. The IGHDs risk model (IGHD1-1, IGHD1-20, IGHD3-16) relates to the OS of AML patients. The nomogram, including risk score and age, can conveniently and effectively predict the overall survival rate of patients.

Long noncoding RNA just proximal to X-inactive specific transcript facilitates aerobic glycolysis and temozolomide chemoresistance by promoting stability of PDK1 mRNA in an m6A-dependent manner in glioblastoma multiforme cells

Improving the chemotherapy resistance of temozolomide (TMZ) is of great significance in the treatment of glioblastoma multiforme (GBM). Long non-coding RNA just proximal to the X-inactive specific transcript (JPX) has been proven to be involved in cancer progression. However, the intrinsic significance and molecular mechanism by which JPX orchestrates GBM progression and TMZ chemotherapy resistance remain poorly understood. Here, JPX was found to be significantly elevated in GBM tissues and cell lines, and patients with high expressions of JPX showed significantly worse prognoses. Functional experiments revealed its carcinogenic roles in GBM cell proliferation, TMZ chemoresistance, anti-apoptosis, DNA damage repair, and aerobic glycolysis. Mechanistically, JPX formed a complex with phosphoinositide dependent kinase-1 (PDK1) messenger RNA (mRNA) and promoted its stability and expression. Furthermore, an RNA immunoprecipitation (RIP) experiment showed that JPX interacted with N6-methyladenosine (m6A) demethylase FTO alpha-ketoglutarate dependent dioxygenase (FTO) and enhanced FTO-mediated PDK1 mRNA demethylation. JPX exerted its GBM-promotion effects through the FTO/PDK1 axis. Taken together, these findings reveal the key role of JPX in promoting GBM aerobic glycolysis and TMZ chemoresistance in an m6A-dependent manner. Thus, it comprises a promising novel therapeutic target for GBM chemotherapy.

Identification of a Seven-lncRNA-mRNA Signature for Recurrence and Prognostic Prediction in Relapsed Acute Lymphoblastic Leukemia Based on WGCNA and LASSO Analyses

Abnormal expressions of long noncoding RNAs (lncRNAs) and protein-encoding messenger RNAs (mRNAs) are important for the development of childhood acute lymphoblastic leukemia (ALL). This study developed an lncRNA-mRNA integrated classifier for the prediction of recurrence and prognosis in relapsed childhood ALL by using several transcriptome data. Weighted gene coexpression network analysis revealed that green, turquoise, yellow, and brown modules were preserved across the TARGET, GSE60926, GSE28460, and GSE17703 datasets, and they were associated with clinical relapse and death status. A total of 184 genes in these four modules were differentially expressed between recurrence and nonrecurrence samples. Least absolute shrinkage and selection operator analysis showed that seven genes constructed a prognostic signature (including one lncRNA: LINC00652 and six mRNAs: INSL3, NIPAL2, REN, RIMS2, RPRM, and SNAP91). Kaplan-Meier curve analysis observed that patients in the high-risk group had a significantly shorter overall survival than those of the low-risk group. Receiver operating characteristic curve analysis demonstrated that this signature had high accuracy in predicting the 5-year overall survival and recurrence outcomes, respectively. LINC00652 may function by coexpressing with the above prognostic genes (INSL3, SNAP91, and REN) and lipid metabolism-related genes (MIA2, APOA1). Accordingly, this lncRNA-mRNA-based classifier may be clinically useful to predict the recurrence and prognosis for childhood ALL. These genes represent new targets to explain the mechanisms for ALL.

Long non-coding RNA LINC01003 suppresses the development of multiple myeloma by targeting miR-33a-5p/PIM1 axis

BACKGROUND

Numerous long non-coding RNAs (lncRNAs) are reported to affect the progression of multiple myeloma (MM). This study is aimed to explore the role and downstream mechanism of lncRNA LINC01003 in MM.

MATERIALS AND METHODS

Xenograft tumor assay was used to assess the function of LINC01003 in MM in vivo. The mRNA expression levels of LINC01003, miR-33a-5p, and PIM1 were determined by quantitative real-time polymerase chain reaction. Cell viability was examined by MTT assay. Relative protein levels of apoptosis-related factors (Bcl-2 and Bax) and proviral integration site of the Moloney leukemia virus kinase 1 (PIM1) were detected via western blot. Adhesion-related proteins were measured by Enzyme-linked immunosorbent assay was used to determine the levels of adhesion-related proteins. Besides, the target relation among LINC01003, miR-33a-5p and PIM1 was tested via dual-luciferase reporter assay.

RESULTS

Low expression of LINC01003 was observed in MM cell lines and peripheral blood samples of MM patients. Both LINC01003 up-regulation and miR-33a-5p down-regulation repressed cell viability and adhesion, and promoted apoptosis of MM cells. Moreover, LINC01003 suppressed the growth of xenograft tumor in mice. We then identified miR-33a-5p as a downstream target of LINC01003, and confirmed that PIM1 was a direct target gene of miR-33a-5p. Both high expression of miR-33a-5p and low expression of PIM1 reversed the suppressive effects of LINC01003 overexpression on cell adhesion and viability, and the promoting effect on apoptosis in MM cells.

CONCLUSION

LINC01003 functioned as a sponge of miR-33a-5p to inhibit the development MM by regulating PIM1 expression.

Comprehensive Analysis of Prognostic Markers for Acute Myeloid Leukemia Based on Four Metabolic Genes

Background: Metabolic reprogramming is the core characteristic of tumors during the development of tumors, and cancer cells can rely on metabolic changes to support their rapid growth. Nevertheless, an overall analysis of metabolic markers in acute myeloid leukemia (AML) is absent and urgently needed. Methods: Within this work, genetic expression, mutation data and clinical data of AML were queried from Genotype-Tissue Expression (GTEx) database, The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The tumor samples of TCGA were randomly divided into a training group (64 samples) and an internal validation group (64 samples) at one time, and the tumor samples of GEO served as two external validation groups (99 samples, 374 samples). According to the expression levels of survival-associated metabolic genes, we divided all TCGA tumor samples into high, medium and low metabolism groups, and evaluated the immune cell activity in the tumor microenvironment of the three metabolism groups by single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, we examined the mutations and prognostic effects of each model gene. Results: Four metabolism-related genes were screened and applied to construct a prognostic model for AML, giving excellent results. As for the area under the curve (AUC) value of receiver operating characteristic (ROC) curve, the training group was up to 0.902 (1-year), 0.81 (3-year), and 0.877 (5-year); and the internal and external validation groups also met the expected standards, showing high potency in predicting patient outcome. Univariate and multivariate prognostic analyses indicated that the riskScore obtained from our prognostic model was an independent prognostic factor. ssGSEA analysis revealed the high metabolism group had higher immune activity. Single and multiple gene survival analysis validated that each model gene had significant effects on the overall survival of AML patients. Conclusions: In our study, a high-efficiency prognostic prediction model was built and validated for AML patients. The results showed that metabolism-related genes could become potential prognostic biomarkers for AML.

Long non-coding RNAs and MYC association in hematological malignancies

Long non-coding RNAs (lncRNAs) have an established role in cell biology. Among their functions is the regulation of hematopoiesis. They characterize the different stages of hematopoiesis in a more lineage-restricted expression pattern than coding mRNAs. They affect hematopoietic stem cell renewal, proliferation, and differentiation of committed progenitors by interacting with master regulators transcription factors. Among these transcription factors, MYC has a prominent role. Similar to MYC's transcriptional activation/amplification of protein coding genes, MYC also regulates lncRNAs' expression profile, while it is also regulated by lncRNAs. Both myeloid and lymphoid malignancies are prone to the association of MYC with lncRNAs. Such interaction inhibits apoptosis, enhances cell proliferation, deregulates metabolism, and promotes genomic instability and resistance to treatment. In this review, we discuss the recent findings that encompass the crosstalk between lncRNAs and describe the pathways that very probably have a pathogenetic role in both acute and chronic hematologic malignancies.